Dose Reconstruction Research Articles

Host microbiome depletion attenuates biofluid metabolite responses following radiation exposure.

Development of novel biodosimetry assays and medical countermeasures is needed to obtain a level of radiation preparedness in the event of malicious or accidental mass exposures to ionizing radiation (IR). For biodosimetry, metabolic profiling with mass spectrometry (MS) platforms has identified several small molecules in easily accessible biofluids that are promising for dose reconstruction. As our microbiome has profound effects on biofluid metabolite composition, it is of interest how variation in the host microbiome may affect metabolomics based biodosimetry. Here, we 'knocked out' the microbiome of male and female C57BL/6 mice (Abx mice) using antibiotics and then irradiated (0, 3, or 8 Gy) them to determine the role of the host microbiome on biofluid radiation signatures (1 and 3 d urine, 3 d serum). Biofluid metabolite levels were compared to a sham and irradiated group of mice with a normal microbiome (Abx-con mice). To compare post-irradiation effects in urine, we calculated the Spearman's correlation coefficients of metabolite levels with radiation dose. For selected metabolites of interest, we performed more detailed analyses using linear mixed effect models to determine the effects of radiation dose, time, and microbiome depletion. Serum metabolite levels were compared using an ANOVA. Several metabolites were affected after antibiotic administration in the tryptophan and amino acid pathways, sterol hormone, xenobiotic and bile acid pathways (urine) and lipid metabolism (serum), with a post-irradiation attenuative effect observed for Abx mice. In urine, dose×time interactions were supported for a defined radiation metabolite panel (carnitine, hexosamine-valine-isoleucine [Hex-V-I], creatine, citric acid, and Nε,Nε,Nε-trimethyllysine [TML]) and dose for N1-acetylspermidine, which also provided excellent (AUROC ≥ 0.90) to good (AUROC ≥ 0.80) sensitivity and specificity according to the area under the receiver operator characteristic curve (AUROC) analysis. In serum, a panel consisting of carnitine, citric acid, lysophosphatidylcholine (LysoPC) (14:0), LysoPC (20:3), and LysoPC (22:5) also gave excellent to good sensitivity and specificity for identifying post-irradiated individuals at 3 d. Although the microbiome affected the basal levels and/or post-irradiation levels of these metabolites, their utility in dose reconstruction irrespective of microbiome status is encouraging for the use of metabolomics as a novel biodosimetry assay.

A novel method for rapid estimation of active bone marrow dose for radiotherapy patients in epidemiological studies.

In a dedicated effort to improve the assessment of clonal hematopoiesis (CH) and study leukemia risk following radiotherapy, we are developing a large-scale cohort study among cancer patients who received radiation. To that end, it will be critical to analyze dosimetric parameters of red bone marrow (ABM) exposure in relation to CH and its progression to myeloid neoplasms, requiring reconstruction method for ABM doses of a large-scale patients rapidly and accurately. To support a large-scale cohort study on the assessment of clonal hematopoiesis and leukemia risk following radiotherapy, we present a new method for the rapid reconstruction of ABM doses of radiotherapy among cancer patients. The key idea of the presented method is to segment patient bones rapidly and automatically by matching a whole-body computational human phantom, in which the skeletal system is divided into 34 bone sites, to patient CT images via 3D skeletal registration. The automatic approach was used to segment site-specific bones for 40 radiotherapy patients. Also, we segmented the bones manually. The bones segmented both manually and automatically were then combined with the patient dose matrix calculated by the treatment planning system (TPS) to derive patient ABM dose. We evaluated the performance of the automatic method in geometric and dosimetric accuracy by comparison with the manual approach. The pelvis showed the best geometric performance [volume overlap fraction (VOF): 52% (mean) with 23% (σ) and average distance (AD): 0.8cm (mean) with 0.5cm (σ)]. The pelvis also showed the best dosimetry performance [absorbed dose difference (ADD): 0.7Gy (mean) with 1.0Gy (σ)]. Some bones showed unsatisfactory performances such as the cervical vertebrae [ADD: 5.2Gy (mean) with 10.8Gy (σ)]. This impact on the total ABM dose, however, was not significant. An excellent agreement for the total ABM dose was indeed observed [ADD: 0.4Gy (mean) with 0.4Gy (σ)]. The computation time required for dose calculation using our method was robust (about one minute per patient). We confirmed that our method estimates ABM doses across treatment sites accurately, while providing high computational efficiency. The method will be used to reconstruct patient-specific ABM doses for dose-response assessment in a large cohort study. The method can also be applied to prospective dose calculation within a clinical TPS to support clinical decision making at the point of care.

1770: On-the-fly dose reconstruction from in-beam PET activation

1770: On-the-fly dose reconstruction from in-beam PET activation

Usability of sand samples in TL Radiation Dosimetry.

This study investigated natural sand thermoluminescence (TL) response as a possible option for retrospective high-dose gamma dosimetry. The natural sand under investigation was collected from six locations with selection criteria for sampling sites covering the highest probability of exposure to unexpected radiation on the Egyptian coast. Dose-response, glow curve, chemical composition, linearity, and fading rate for different sand samples were studied. Energy Dispersive X-ray Spectroscopy (EDX) analysis revealed differences in chemical composition among the various geological sites, leading to variations in TL glow curve intensity. Sand samples collected from Ras Sedr, Taba, Suez, and Enshas showed similar TL patterns, although with different TL intensities. Beach sands of Matrouh and North Coastal with a high calcite content did not show a clear linear response to the TL technique, in the dose range of 10Gy up to 30kGy. The results show that most sand samples are suitable as a radiation dosimeter at accidental levels of exposure. It is proposed here that for high-dose gamma dosimetry with doses ranging from 3 to 10kGy, a single calibration factor might be enough for TL measurements using sand samples. However, proper calibration might allow dose assessment for doses even up to 30kGy. Most of the investigated sand samples had nearly stable fading rates after seven days of storage. The Ras Sedr sands sample was the most reliable for retrospective dose reconstruction.

Retrospective EPR dosimetry in glass and tooth enamel – The methodology and results of MUG group in RENEB 2021 inter-laboratory comparison

Electron Paramagnetic Resonance (EPR) is recognized method in retrospective dosimetry, based on detection of stable paramagnetic centres induced by radiation in biological materials, like enamel, bones, nails, or artificial materials, like glasses and other. EPR dosimetry in glass and enamel were included, in addition to several biological dosimetry tests, in the recent intercomparison project RENEB ILC 2021 as one of the physical dosimetry methods potentially applicable for dose assessment in radiation accidents. EPR group from Medical University of Gdansk (MUG) was one of participants in this intercomparison. The participants reconstructed doses in three blinded samples of watch glass, Gorilla Glass from phone screens and in enamel irradiated with 0 Gy, 1.2 Gy and 3.5 Gy (these doses were revealed after reporting the final results by all participants). For reconstruction of doses in the glass samples the respective calibration samples were provided by organizers of the ILC project. For dosimetry in enamel the participants used their own calibration data. The doses reconstructed by us in the blinded samples (in terms of kerma in air) were: in watch glass: −0.05 Gy, 1.03 Gy, 3.16 Gy, in Gorilla Glass: −0.03 Gy, 1.20 Gy, 3.44 Gy, in enamel: 0.1 Gy, 1.3 Gy, 3.1 Gy. The differences between the reconstructed and the nominal doses were small and the accuracy was sufficient for triage of irradiated people at the level of 1–2 Gy. The small sample mass of the enamel, suitable rather to Q-band EPR dosimetry and potentially available without whole tooth extraction, allowed to obtain satisfactory accuracy of EPR dosimetry also in the X-band measurements.

Chasing snails: Automating the processing of EMCCD images of luminescence from opercula

Opercula of the gastropod Bithynia tentaculata are composed of calcite, and are typically 2–4 mm in length. They emit a thermoluminescence (TL) signal that can be used for dose reconstruction, and spatially resolved TL data from them can be obtained using an electron multiplying charge coupled device (EMCCD). However, when multiple measurements are made of the same sample with imaging detectors such as the EMCCD, registering the different images is crucial so that when regions of interest (ROI) are defined they consistently relate to the same part of the specimen. Previous work on opercula has undertaken this registration by hand, but this is prohibitively time consuming and is also potentially prone to human error. An automated registration process is described, and its use is illustrated using a dose recovery experiment. Without registration more than half of the regions of interest defined across the operculum failed the recycling test, and for those ROIs which did pass recycling the dose recovery ratio varied from 0.7 to 1.2. After registration more than 97% of ROIs passed recycling and all these ROIs gave dose recovery ratios within two sigma of unity. The automated registration process described here has potential for application to other types of solid sample such as rock slices provided they are not perfectly circular.

Respiratory motion modelling for MR-guided lung cancer radiotherapy: model development and geometric accuracy evaluation.

Objective.Respiratory motion of lung tumours and adjacent structures is challenging for radiotherapy. Online MR-imaging cannot currently provide real-time volumetric information of the moving patient anatomy, therefore limiting precise dose delivery, delivered dose reconstruction, and downstream adaptation methods.Approach.We tailor a respiratory motion modelling framework towards an MR-Linac workflow to estimate the time-resolved 4D motion from real-time data. We develop a multi-slice acquisition scheme which acquires thick, overlapping 2D motion-slices in different locations and orientations, interleaved with 2D surrogate-slices from a fixed location. The framework fits a motion model directly to the input data without the need for sorting or binning to account for inter- and intra-cycle variation of the breathing motion. The framework alternates between model fitting and motion-compensated super-resolution image reconstruction to recover a high-quality motion-free image and a motion model. The fitted model can then estimate the 4D motion from 2D surrogate-slices. The framework is applied to four simulated anthropomorphic datasets and evaluated against known ground truth anatomy and motion. Clinical applicability is demonstrated by applying our framework to eight datasets acquired on an MR-Linac from four lung cancer patients.Main results.The framework accurately reconstructs high-quality motion-compensated 3D images with 2 mm3isotropic voxels. For the simulated case with the largest target motion, the motion model achieved a mean deformation field error of 1.13 mm. For the patient cases residual error registrations estimate the model error to be 1.07 mm (1.64 mm), 0.91 mm (1.32 mm), and 0.88 mm (1.33 mm) in superior-inferior, anterior-posterior, and left-right directions respectively for the building (application) data.Significance.The motion modelling framework estimates the patient motion with high accuracy and accurately reconstructs the anatomy. The image acquisition scheme can be flexibly integrated into an MR-Linac workflow whilst maintaining the capability of online motion-management strategies based on cine imaging such as target tracking and/or gating.

A systematic assessment and optimization of photon-counting CT for lung density quantifications.

Photon-counting computed tomography (PCCT) has recently emerged into clinical use; however, its optimum imaging protocols and added benefits remains unknown in terms of providing more accurate lung density quantification compared to energy-integrating computed tomography (EICT) scanners. To systematically assess the performance of a clinical PCCT scanner for lung density quantifications and compare it against EICT. This cross-sectional study involved a retrospective analysis of subjects scanned (August-December 2021) using a clinical PCCT system. The influence of altering reconstruction parameters was studied (reconstruction kernel, pixel size, slice thickness). A virtual CT dataset of anthropomorphic virtual subjects was acquired to demonstrate the correspondence of findings to clinical dataset, and to perform systematic imaging experiments, not possible using human subjects. The virtual subjects were imaged using a validated, scanner-specific CT simulator of a PCCT and two EICT (defined as EICT A and B) scanners. The images were evaluated using mean absolute error (MAE) of lung and emphysema density against their corresponding ground truth. Clinical and virtual PCCT datasets showed similar trends, with sharper kernels and smaller voxel sizes increasing percentage of low-attenuation areas below -950 HU (LAA-950) by up to 15.7±6.9% and 11.8±5.5%, respectively. Under the conditions studied, higher doses, thinner slices, smaller pixel sizes, iterative reconstructions, and quantitative kernels with medium sharpness resulted in lower lung MAE values. While using these settings for PCCT, changes in the dose level (13 to 1.3 mGy), slice thickness (0.4 to 1.5mm), pixel size (0.49 to 0.98mm), reconstruction technique (70keV-VMI to wFBP), and kernel (Qr48 to Qr60) increased lung MAE by 15.3±2.0, 1.4±0.6, 2.2±0.3, 4.2±0.8, and 9.1±1.6 HU, respectively. At the optimum settings identified per scanner, PCCT images exhibited lower lung and emphysema MAE than those of EICT scanners (by 2.6±1.0 and 9.6±3.4 HU, compared to EICT A, and by 4.8±0.8 and 7.4±2.3 HU, compared to EICT B). The accuracy of lung density measurements was correlated with subjects' mean lung density (p<0.05), measured by PCCT at optimum setting under the conditions studied. Photon-counting CT demonstrated superior performance in density quantifications, with its influences of imaging parameters in line with energy-integrating CT scanners. The technology offers improvement in lung quantifications, thus demonstrating potential toward more objective assessment of respiratory conditions.

Reproducible spectral CT thermometry with liver-mimicking phantoms for image-guided thermal ablation

Objectives. Evaluate the reproducibility, temperature tolerance, and radiation dose requirements of spectral CT thermometry in tissue-mimicking phantoms to establish its utility for non-invasive temperature monitoring of thermal ablations. Methods. Three liver mimicking phantoms embedded with temperature sensors were individually scanned with a dual-layer spectral CT at different radiation dose levels during heating (35 °C–80 °C). Physical density maps were reconstructed from spectral results using varying reconstruction parameters. Thermal volumetric expansion was then measured at each temperature sensor every 5 °C in order to establish a correlation between physical density and temperature. Linear regressions were applied based on thermal volumetric expansion for each phantom, and coefficient of variation for fit parameters was calculated to characterize reproducibility of spectral CT thermometry. Additionally, temperature tolerance was determined to evaluate effects of acquisition and reconstruction parameters. The resulting minimum radiation dose to meet the clinical temperature accuracy requirement was determined for each slice thickness with and without additional denoising. Results. Thermal volumetric expansion was robustly replicated in all three phantoms, with a correlation coefficient variation of only 0.43%. Similarly, the coefficient of variation for the slope and intercept were 9.6% and 0.08%, respectively, indicating reproducibility of the spectral CT thermometry. Temperature tolerance ranged from 2 °C to 23 °C, decreasing with increased radiation dose, slice thickness, and iterative reconstruction level. To meet the clinical requirement for temperature tolerance, the minimum required radiation dose ranged from 20, 30, and 57 mGy for slice thickness of 2, 3, and 5 mm, respectively, but was reduced to 2 mGy with additional denoising. Conclusions. Spectral CT thermometry demonstrated reproducibility across three liver-mimicking phantoms and illustrated the clinical requirement for temperature tolerance can be met for different slice thicknesses. The reproducibility and temperature accuracy of spectral CT thermometry enable its clinical application for non-invasive temperature monitoring of thermal ablation.

OSL at elevated temperature of smart chip cards for retrospective dosimetry

Smart chip cards are a promising material in retrospective dosimetry due to their high collectability with low replacement cost. In this study, dose reconstruction using OSL at elevated temperatures for various chip cards was investigated. The approximate spectral emission of the chip cards was determined through a comparison of radiation-induced and intrinsic background TL signals using different detection filters. In the OSL decay curve, a fast component (0.0–0.4 s) was integrated to avoid high intrinsic background signals. To minimize sensitivity changes due to heat treatment, a protocol to measure OSL at 100 °C was developed and the data were compared with those from a similar protocol for OSL measured at room temperature. In the 0.1 - 5 Gy range, linear and power functions were fitted for room temperature and 100 °C OSL protocols, respectively. The minimum detectable dose was evaluated immediately after exposure, with values of 25 mGy for room temperature and 240 mGy for 100 °C OSL being determined. Signal fading was measured up to 30 days after irradiation for the investigated protocols. A dose recovery test was made 10 days after irradiation using various chip cards, and the 100 °C OSL protocol produced more reliable results than the room temperature OSL protocol. As a result, the study emphasizes the necessity of further investigation of OSL at high temperatures using chip cards.

Reconstruction of organ doses for patients undergoing computed tomography examinations in Canada 1992-2019.

We derived the first comprehensive organ dose library for Canadian pediatric and adult patients who underwent computed tomography (CT) scans between 1992 and 2019 to support epidemiological analysis of radiation risk. We calculated organ absorbed doses for Canadian CT patients in two steps. First, we modeled Computed Tomography Dose Index (CTDI) values by patient age, scan body part, and scan year for the scan period between 1992 and 2019 using national survey data conducted in Canada and partially the United Kingdom survey data as surrogates. Second, we converted CTDI values to organ absorbed doses using a library of organ dose conversion coefficients built in an organ dose calculation program, the National Cancer Institute dosimetry system for CT. In result, we created a library of doses delivered to 33 organs and tissues by different patient ages and genders, scan body parts and scan years. In the scan period before 2000, the organs receiving the greatest dose in the head, chest and abdomen-pelvis scans were the active marrow (3.7-15.2mGy), lungs (54.7-62.8mGy) and colon (54.9-68.5mGy), respectively. We observed organ doses reduced by 24% (pediatric head and torso scans, and adult head scans) and 55% (adult torso scans) after 2000. The organ dose library will be used to analyse the risk of radiation exposure from CT scans in the Canadian CT patient cohort.

Deep learning-based tools to distinguish plan-specific from generic deviations in EPID-based in vivo dosimetry.

Dose distributions calculated with electronic portal imaging device (EPID)-based in vivo dosimetry (EIVD) differ from planned dose distributions due to generic and plan-specific deviations. Generic deviations are characteristic to a class of plans. Examples include limitations in EIVD dose reconstruction, inaccuracies in treatment planning system (TPS) calculations and systematic machine deviations. Plan-specific deviations have an unpredictable character. Examples include discrepancies between the patient model used for dose calculation and the patient position or anatomy during delivery, random machine deviations, and data transfer, human or software errors. During the inspection work performed with traditional γ-evaluation statistical methods: (i) generic deviations raise alerts that need to be inspected but that rarely lead to action as their root cause is usually understood and (ii) the detection of relevant plan-specific deviations may be hindered by the presence of generic deviations. To investigate whether deep learning-based tools can help in identifying γ-alerts raised by generic deviations and in improving the detectability of plan-specific deviations. A 3D U-Net was trained as an autoencoder to reconstruct underlying patterns of generic deviations in γ-distributions. The network was trained for four treatment disease sites differently affected by generic deviations: volumetric modulated arc therapy (VMAT) lung (no known deviations), VMAT prostate (TPS inaccuracies), VMAT head-and-neck (EIVD limitations) and intensity modulated radiation therapy (IMRT) breast (large EIVD limitations). The network was trained with virtual non-transit γ-distributions: 60 train/10 validation for the VMAT sites and 30 train/10 validation for IMRT breast. It was hypothesized that in vivo γ-distributions obtained in the presence of plan-specific deviations would differ from those seen during training. For each disease site, the sensitivity of γ-analysis and the network to detect (synthetically introduced) patient-related deviations was compared by receiver operator characteristic analysis. The investigated deviations were patient positioning errors, weight gain or loss, and tumor volume changes. The clinical relevance was illustrated qualitatively with 793 in vivo clinical cases (141 lung, 136 head-and-neck, 209 prostate and 307 breast). Error detectability of patient-related deviations was better with the network than with γ-analysis. The average area under the curve values over all sites were 0.86±0.12(1SD) and 0.69±0.25(1SD), respectively. Regarding in vivo clinical results, the percentage of cases differently classified by γ-analysis and the network was 1%, 19%, 18% and 64% for lung, head-and-neck, prostate, and breast, respectively. In head-and-neck and breast cases, 45 γ-only alerts were examined, of which 43 were attributed to EPID dose reconstruction limitations. For prostate, all 15 investigated γ-only alerts were due to known TPS inaccuracies. All 59 investigated network alerts were explained by either patient-related deviations or EPID acquisition incidents. Some patient-related deviations detected by the network were not detected by γ-analysis. Deep learning-based tools trained to reconstruct underlying patterns of generic deviations in γ-distributions can be used to (i) automatically identify false positives within the set of γ-alerts and (ii) improve the detection of plan-specific deviations, hence minimizing the likelihood of false negatives. The presented method provides clear additional value to the γ-alert management process for large scale EIVD systems.

Frequentist model averaging for analysis of dose-response in epidemiologic studies with complex exposure uncertainty.

In epidemiologic studies, association estimates of an exposure with disease outcomes are often biased when the uncertainties of exposure are ignored. Consequently, corresponding confidence intervals (CIs) will not have correct coverage. This issue is particularly problematic when exposures must be reconstructed from physical measurements, for example, for environmental or occupational radiation doses that were received by a study population for which radiation doses cannot be measured directly. To incorporate complex uncertainties in reconstructed exposures, the two-dimensional Monte Carlo (2DMC) dose estimation method has been proposed and used in various dose reconstruction efforts. The 2DMC method generates multiple exposure realizations from dosimetry models that incorporate various sources of errors to reflect the uncertainty of the dose distribution as well as the uncertainties in individual doses in the exposed population. Traditional measurement-error model approaches, typically based on using mean doses in the dose-exposure analysis, do not fully account exposure uncertainties. A recently developed statistical approach that overcomes many of these limitations by analyzing multiple exposure realizations in relation to disease risk is Bayesian model averaging (BMA). The analytic advantage of the BMA is its ability to better accommodate complex exposure uncertainty in the risk estimation, but a practical. Drawback is its significant computational complexity. In this present paper, we propose a novel frequentist model averaging (FMA) approach which has all the analytical advantages of the BMA method but is much simpler to implement and computationally faster. We show in simulations that, like BMA, FMA yields 95% confidence intervals for association parameters that close to 95% coverage rate. In simulations, the FMA has shorter length of CIs than those of another frequentist approach, the corrected information matrix (CIM) method. We illustrate the similarities in performance of BMA and FMA from a study of exposures from radioactive fallout in Kazakhstan.

Continuous time-resolved estimated synthetic 4D-CTs for dose reconstruction of lung tumor treatments at a 0.35 T MR-linac

Objective. To experimentally validate a method to create continuous time-resolved estimated synthetic 4D-computed tomography datasets (tresCTs) based on orthogonal cine MRI data for lung cancer treatments at a magnetic resonance imaging (MRI) guided linear accelerator (MR-linac). Approach. A breathing porcine lung phantom was scanned at a CT scanner and 0.35 T MR-linac. Orthogonal cine MRI series (sagittal/coronal orientation) at 7.3 Hz, intersecting tumor-mimicking gelatin nodules, were deformably registered to mid-exhale 3D-CT and 3D-MRI datasets. The time-resolved deformation vector fields were extrapolated to 3D and applied to a reference synthetic 3D-CT image (sCTref), while accounting for breathing phase-dependent lung density variations, to create 82 s long tresCTs at 3.65 Hz. Ten tresCTs were created for ten tracked nodules with different motion patterns in two lungs. For each dataset, a treatment plan was created on the mid-exhale phase of a measured ground truth (GT) respiratory-correlated 4D-CT dataset with the tracked nodule as gross tumor volume (GTV). Each plan was recalculated on the GT 4D-CT, randomly sampled tresCT, and static sCTref images. Dose distributions for corresponding breathing phases were compared in gamma (2%/2 mm) and dose–volume histogram (DVH) parameter analyses. Main results. The mean gamma pass rate between all tresCT and GT 4D-CT dose distributions was 98.6%. The mean absolute relative deviations of the tresCT with respect to GT DVH parameters were 1.9%, 1.0%, and 1.4% for the GTV D 98%, D 50%, and D 2%, respectively, 1.0% for the remaining nodules D 50%, and 1.5% for the lung V 20Gy. The gamma pass rate for the tresCTs was significantly larger (p < 0.01), and the GTV D 50% deviations with respect to the GT were significantly smaller (p < 0.01) than for the sCTref. Significance. The results suggest that tresCTs could be valuable for time-resolved reconstruction and intrafractional accumulation of the dose to the GTV for lung cancer patients treated at MR-linacs in the future.

MVCT image enhancement using reference-based encoder–decoder convolutional neural network

Daily MVCT (Megavoltage Computed Tomography) in TomoTherapy plays a crucial role in patients’ setup and dose reconstruction. However, MVCT images suffer from high noise and low tissue contrast due to the limited number of X-ray photons and low detector quantum efficiency (DQE). In this study, we propose an approach to enhance MVCT images using the KVCT (Kilovoltage Computed Tomography) image of the same patient as an auxiliary reference image. Specifically, we propose a reference-based encoder–decoder convolutional neural network (RefED-CNN) by incorporating a feature extraction and alignment (FEA) module to introduce the features of the reference image as side information into the MVCT image enhancement process. The FEA module automatically searches and aligns relevant features between the reference image and the noisy image, and transfers the relevant texture from reference KVCT images to the target MVCT image. Evaluations conducted on both phantom and real patient data show that our method outperforms other denoising methods by effectively reducing noise and preserving intricate structural details.

PC-01.30 - DOSE RECONSTRUCTION FOR VMAT TREATMENT PLANS USING CBCT AND MACHINE LOG-FILE: PRELIMINARY RESULTS

PC-01.30 - DOSE RECONSTRUCTION FOR VMAT TREATMENT PLANS USING CBCT AND MACHINE LOG-FILE: PRELIMINARY RESULTS

Real-time motion-including dose estimation of simulated multi-leaf collimator-tracked magnetic resonance-guided radiotherapy.

Real-time dose estimation is a key-prerequisite to enable online intra-fraction treatment adaptation in magnetic resonance (MR)-guided radiotherapy (MRgRT). It is an essential component for the assessment of the dosimetric benefits and risks of online adaptive treatments, such as multi-leaf collimator (MLC)-tracking. We present a proof-of-concept for a software workflow for real-time dose estimation of MR-guided adaptive radiotherapy based on real-time data-streams of the linac delivery parameters and target positions. A software workflow, combining our in-house motion management software DynaTrack, a real-time dose calculation engine that connects to a research version of the treatment planning software (TPS) Monaco (v.6.09.00, Elekta AB, Stockholm, Sweden) was developed and evaluated. MR-guided treatment delivery on the Elekta Unity MR-linac was simulated with and without MLC-tracking for three prostate patients, previously treated on the Elekta Unity MR-linac (36.25Gy/five fractions). Three motion scenarios were used: no motion, regular motion, and erratic prostate motion. Accumulated monitor units (MUs), centre of mass target position and MLC-leaf positions, were forwarded from DynaTrack at a rate of 25Hz to a Monte Carlo (MC) based dose calculation engine which utilises the research GPUMCD-library (Elekta AB, Stockholm, Sweden). A rigid isocentre shift derived from the selected motion scenarios was applied to a bulk density-assigned session MR-image. The respective electron density used for treatment planning was accessed through the research Monaco TPS. The software workflow including the online dose reconstruction was validated against offline dose reconstructions. Our investigation showed that MC-based real-time dose calculations that account for all linac states (including MUs, MLC positions and target position) were infeasible, hence states were randomly sampled and used for calculation as follows; Once a new linac state was received, a dose calculation with 106 photons was started. Linac states that arrived during the time of the ongoing calculation were put into a queue. After completion of the ongoing calculation, one new linac state was randomly picked from the queue and assigned the MU accumulated from the previous state until the last sample in the queue. The queue was emptied, and the process repeated throughout treatment simulation. On average 27% (23%-30%) of received samples were used in the real-time calculation, corresponding to a calculation time for one linac state of 148ms. Median gamma pass rate (2%/3mm local) was 100.0% (99.9%-100%) within the PTV volume and 99.1% (90.1%-99.4.0%) with a 15% dose cut off. Differences in PTVDmean , CTVDmean , RectumD2% , and BladderD2% (offline-online, % of prescribed dose) were below 0.64%. Beam-by-beam comparisons showed deviations below 0.07Gy. Repeated simulations resulted in standard deviations below 0.31% and 0.12Gy for the investigated volume and dose criteria respectively. Real-time dose estimation was successfully performed using the developed software workflow for different prostate motion traces with and without MLC-tracking. Negligible dosimetric differences were seen when comparing online and offline reconstructed dose, enabling online intra-fraction treatment decisions based on estimates of the delivered dose.

Diagnostic Accuracy of the Coronary Calcium Score Acquired with Reduced Radiation Dose and Iterative Reconstruction

Diagnostic Accuracy of the Coronary Calcium Score Acquired with Reduced Radiation Dose and Iterative Reconstruction

Development of Population-Based Pediatric Computational Phantoms for Radiotherapy Applications.

Age-specific pediatric computational phantoms are used in radiotherapy (RT) for quality assurance and for reconstruction of historical RT doses (within others). Phantoms are typically developed from healthy patients and may not effectively represent those with cancer due to pathology and/or treatment effects. This study evaluated a set of population-based pediatric computational phantoms developed in-house in terms of anatomical plausibility. Planning CTs and contours from historical craniospinal irradiation (CSI) patients (n = 74, median age 7y, range: 1-17y) were used to generate and evaluate a set of in-house age-specific population-based RT phantoms (RT-P). The RT-P were generated by combining a sub-set of clinical CTs and contours through groupwise deformable image registration, generating average models of CSI sub-populations (n = 74, median age 7y, range: 3-14y). Models were then compared against clinical data and two libraries of phantoms representing healthy populations: the International Commission on Radiological Protection (ICRP) pediatric reference computational phantoms (n = 8, median age 8y, range: 1-15y) and a variety of default 4D extended cardiac torso (XCAT) phantoms (n = 75, median age 9y, range: 1-18y). Variation between organ volumes for the different datasets was assessed through a linear fit of organ volume with age, reporting the slope (∑) of each fit [y-1]. Average difference between the volume datapoints and the linear fit for clinical data (Δ) [%] were also reported. This allowed for comparisons of the RT-P to clinical and reference data in terms of organ volumes across developmental stages. The table shows 9 of the 19 investigated organs. The ∑ reported for RT-P models were of similar magnitude as the clinical data and other phantoms, effectively modelling changes with age. The greatest and least ∑ were reported from lungs and thyroid respectively, in agreement with expected relative sizes between organs. Larger values for Δ were likely due to differences in organ filling and segmentation strategy between datasets, limitations of RT-P methodology, and/or anatomical differences between healthy and cancer populations. The RT-P models show promise in representing the RT cohort that may benefit from specialized anatomical phantoms. Further work is needed to address the limitations of the current methodology and its applicability to other RT cohorts.

Deep Learning-Based Single-View Fluorescence Dose Reconstruction for 3D Dosimetry

3D dose distribution measurement is crucial for precise radiotherapy. Radiation-excited fluorescence imaging has potential for the 3D dosimetry with high spatial resolution, but multiple fluorescence images from different view-angles are required for analytical reconstruction techniques. Furthermore, the imaging data are contaminated by anisotropic Cherenkov light emission and statistical noise. This project aims to establish a novel deep learning-based model to predict 3D dose distributions from a single-view 2D fluorescence image while simultaneously removing the adverse effects of Cherenkov signals and other noises. A total of 124 single-aperture static photon beams were delivered to an acrylic tank containing 1 g/L quinine hemisulfate water solution with varying aperture shapes and collimator angle. The emitted optical signals were detected by a low-cost CMOS camera for 20 seconds, and image pre-processing was performed to obtain input 2D fluorescence images with 0.3 × 0.3 mm spatial resolution. 3D back-projected dose distribution images were also calculated from the input fluorescence images. Ground-truth of 3D dose distributions and 2D field map images were obtained from a clinical treatment planning system with 1.4 × 1.4 × 1.4 mm spatial resolution. The proposed deep learning-based dose reconstruction method involved 3 steps. First, 2D fluence map images at the bottom plane of the tank were predicted from the fluorescence images by using a customized convolutional neural network (CNN). Second, the predicted fluence map images were transformed into the 2D field map images on the isocenter plane by applying perspective transformation. Finally, 2D dose distributions at a given radiological depth were calculated by using the predicted field map images, the back-projected dose distribution images, and the radiological depth value as inputs of a shallow CNN. Both CNN models were trained separately, and the 3D dose distributions were predicted by concatenating the output 2D dose distributions at various radiological depths. The proposed CNN model yielded accurate 2D field map images. Averaged Dice similarity coefficient and mean absolute error of the field maps in the test data was 92.0% ± 4.6% and 0.0132 ± 0.0113, respectively. Moreover, our deep learning-based approach was able to predict accurate 3D dose distributions from the 2D fluorescence images. Mean squared error and averaged 3D gamma passing ratio (3%/3mm) were 9.55 mGy ± 6.8 mGy and 86.3% ± 9.86%, respectively. Theproposed deep learning-based method calculated accurate 3D dose distributions from a single-view 2D fluorescence image. Since this technique require only a single CMOS camera image and fluorescent material, it can be readily used for any external radiotherapy modalities, including SRS/SBRT with small fields. This method is useful for acquiring 3D dose distribution data for precise dose verification within a few seconds.